SlideShare a Scribd company logo
1 of 46
Web Analytics Jim Jansen Associate Professor, The Pennsylvania State University
Who is Jim Jansen? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Explosion of Information - the  Zettabytes  are coming There will be nearly  15 billion devices  connected to the Internet, generating nearly a   Zettabyte  (one sextillion bytes) of global IP traffic by 2015, Cisco's fifth annual Visual Networking Index (VNI) Forecast
How much is a Zettabyte?
[object Object],[object Object],[object Object],[object Object],Explosion of Information - the  Zettabytes  are coming There will be nearly  15 billion devices  connected to the Internet, generating nearly a  Zettabyte  (one sextillion bytes) of global IP traffic by 2015, Cisco's fifth annual Visual Networking Index (VNI) Forecast
Web analytics can help us … ,[object Object],[object Object],[object Object],[object Object],How does web analytics do this?
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Data    Information    Knowledge This is the realm of Web analytics!
What is web analytics? ,[object Object],[object Object],[object Object],[object Object]
Let’s break that definition down …  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Data Information Knowledge
[object Object]
W3C Extended Log Format -Variety of fields for examining visitors to Web sites. Other common format is  NCSA   Separate Log  that is composed of three logs  Common log  – actions on the server,  Referral log  – where they came from, and  Agent log  – stuff about the client computer Rather than service-side logging, other methods such as page tagging, image cookies, Flash cookies, etc. but the data is still stored in a log.  W3C Extended Log Format
[object Object],[object Object]
Variety of tools help make sense of this log data
[object Object]
Theoretical Foundations ,[object Object],[object Object],[object Object],Burrhus Frederic Skinner  John B. Watson  Ivan Petrovich Pavlov
Behaviorism Characteristics ,[object Object],[object Object],[object Object],[object Object]
What is a Behavior? ,[object Object],[object Object],[object Object],[object Object],[object Object]
What is a Behavior? ,[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],Data Collection: Trace Data Wear on a carpet Trash heap Surfing web
Trace Data ,[object Object],[object Object],[object Object],[object Object],What is  cool  about  trace data  for researchers?
Data Collection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Methodological Foundations ,[object Object],[object Object],[object Object],Customer Behavior (video) Chemistry (surface marking)
Methodological Foundations ,[object Object],[object Object],[object Object],[object Object],[object Object],Example: ethnography studies (where the researcher “bird dogs” a study participant Example: no one searches for porn in a lab study of Web searching Example: is why medical trials are double blind rather than single blind
Methodological Foundations ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object]
Web analytics process  ,[object Object],[object Object]
Essential steps to any effective web analytics process  Typically counts. Basically, data collection ,[object Object],[object Object],[object Object],[object Object],Typically ratios. Data becomes metrics. Counts and ratios infused with business strategy. Online goals, objectives, or standards for organization. ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Collection of  data Processing of data into information Developing key  performance  indicators Formulating online strategy Drives Drives Drives Drives Drives Drives
Three types ( plus 1 ) of Web analytics metrics Implementation ,[object Object],[object Object],[object Object],[object Object],Burby, J., Brown, A., and WAA Standard Committee (2007) Web Analytics Definitions. Web Analytics Association. Available at: http://www.webanalyticsassociation.org/resource/resmgr/PDF_standards/WebAnalyticsDefinitionsVol1.pdf
Can be applied to three levels of granularity ,[object Object],[object Object],[object Object],Burby, J., Brown, A., and WAA Standard Committee (2007) Web Analytics Definitions. Web Analytics Association. Available at: http://www.webanalyticsassociation.org/resource/resmgr/PDF_standards/WebAnalyticsDefinitionsVol1.pdf
Classifications of Metrics ,[object Object],[object Object],[object Object],[object Object],Burby, J., Brown, A., and WAA Standard Committee (2007) Web Analytics Definitions. Web Analytics Association. Available at: http://www.webanalyticsassociation.org/resource/resmgr/PDF_standards/WebAnalyticsDefinitionsVol1.pdf
Building Block ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Burby, J., Brown, A., and WAA Standard Committee (2007) Web Analytics Definitions. Web Analytics Association. Available at: http://www.webanalyticsassociation.org/resource/resmgr/PDF_standards/WebAnalyticsDefinitionsVol1.pdf
Visit Characteristics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Burby, J., Brown, A., and WAA Standard Committee (2007) Web Analytics Definitions. Web Analytics Association. Available at: http://www.webanalyticsassociation.org/resource/resmgr/PDF_standards/WebAnalyticsDefinitionsVol1.pdf
Content Characterization ,[object Object],[object Object],[object Object],[object Object],Burby, J., Brown, A., and WAA Standard Committee (2007) Web Analytics Definitions. Web Analytics Association. Available at: http://www.webanalyticsassociation.org/resource/resmgr/PDF_standards/WebAnalyticsDefinitionsVol1.pdf
Conversion Metrics ,[object Object],[object Object],Burby, J., Brown, A., and WAA Standard Committee (2007) Web Analytics Definitions. Web Analytics Association. Available at: http://www.webanalyticsassociation.org/resource/resmgr/PDF_standards/WebAnalyticsDefinitionsVol1.pdf
Translating these metrics ,[object Object],[object Object]
The hotel ,[object Object],Sam Ted Jane Sam Scott Jane Sam Ara Sam Chi Sam Tom Sam Yen Sam Tim Jane Jane Jane Jane Jane Rooms 1  2  3 Days 1  2  3  4  5  6  7 3 3 3 3 3 3 3 ,[object Object],[object Object],1 1 Count Count 7 ,[object Object]
Bottom line: the time qualifier matters! ,[object Object],[object Object],[object Object]
50 minutes = Can’t Cover Everything ,[object Object]
Research Work (mine) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Research Work (mine) ,[object Object]
Great ‘how to books’ for web analytics ,[object Object],[object Object],[object Object],[object Object]
Thanks! (welcome questions / discussion!) Web Analytics Jim Jansen Associate Professor, The Pennsylvania State University
[object Object]
Follow-on Discussion ,[object Object],[object Object],[object Object],[object Object]
Again, thanks! Web Analytics Jim Jansen Associate Professor, The Pennsylvania State University

More Related Content

What's hot

accelerating-data-driven
accelerating-data-drivenaccelerating-data-driven
accelerating-data-driven
Joshua Chudy
 
Research Metadata Mechanics - Simon Porter
Research Metadata Mechanics - Simon PorterResearch Metadata Mechanics - Simon Porter
Research Metadata Mechanics - Simon Porter
CASRAI
 

What's hot (20)

Slides | Research data literacy and the library
Slides | Research data literacy and the librarySlides | Research data literacy and the library
Slides | Research data literacy and the library
 
The Rensselaer IDEA: Data Exploration
The Rensselaer IDEA: Data Exploration The Rensselaer IDEA: Data Exploration
The Rensselaer IDEA: Data Exploration
 
Machines are people too
Machines are people tooMachines are people too
Machines are people too
 
In search of lost knowledge: joining the dots with Linked Data
In search of lost knowledge: joining the dots with Linked DataIn search of lost knowledge: joining the dots with Linked Data
In search of lost knowledge: joining the dots with Linked Data
 
How to Execute A Research Paper
How to Execute A Research PaperHow to Execute A Research Paper
How to Execute A Research Paper
 
Slides | Targeting the librarian’s role in research services
Slides | Targeting the librarian’s role in research servicesSlides | Targeting the librarian’s role in research services
Slides | Targeting the librarian’s role in research services
 
Data Science and What It Means to Library and Information Science
Data Science and What It Means to Library and Information ScienceData Science and What It Means to Library and Information Science
Data Science and What It Means to Library and Information Science
 
OpenML data@Sheffield
OpenML data@SheffieldOpenML data@Sheffield
OpenML data@Sheffield
 
The Roots: Linked data and the foundations of successful Agriculture Data
The Roots: Linked data and the foundations of successful Agriculture DataThe Roots: Linked data and the foundations of successful Agriculture Data
The Roots: Linked data and the foundations of successful Agriculture Data
 
Tragedy of the (Data) Commons
Tragedy of the (Data) CommonsTragedy of the (Data) Commons
Tragedy of the (Data) Commons
 
Data Analytics
Data AnalyticsData Analytics
Data Analytics
 
Linking Data to Publications through Citation and Virtual Archives
Linking Data to Publications through Citation and Virtual ArchivesLinking Data to Publications through Citation and Virtual Archives
Linking Data to Publications through Citation and Virtual Archives
 
No Free Lunch: Metadata in the life sciences
No Free Lunch:  Metadata in the life sciencesNo Free Lunch:  Metadata in the life sciences
No Free Lunch: Metadata in the life sciences
 
accelerating-data-driven
accelerating-data-drivenaccelerating-data-driven
accelerating-data-driven
 
Emerging Data Citation Infrastructure
Emerging Data Citation InfrastructureEmerging Data Citation Infrastructure
Emerging Data Citation Infrastructure
 
Data, Data Everywhere: What's A Publisher to Do?
Data, Data Everywhere: What's  A Publisher to Do?Data, Data Everywhere: What's  A Publisher to Do?
Data, Data Everywhere: What's A Publisher to Do?
 
Research Metadata Mechanics - Simon Porter
Research Metadata Mechanics - Simon PorterResearch Metadata Mechanics - Simon Porter
Research Metadata Mechanics - Simon Porter
 
Linked data presentation for libraries (COMO)
Linked data presentation for libraries (COMO)Linked data presentation for libraries (COMO)
Linked data presentation for libraries (COMO)
 
NISO Virtual Conference Scientific Data Management: Caring for Your Instituti...
NISO Virtual Conference Scientific Data Management: Caring for Your Instituti...NISO Virtual Conference Scientific Data Management: Caring for Your Instituti...
NISO Virtual Conference Scientific Data Management: Caring for Your Instituti...
 
Prov-O-Viz: Interactive Provenance Visualization
Prov-O-Viz: Interactive Provenance VisualizationProv-O-Viz: Interactive Provenance Visualization
Prov-O-Viz: Interactive Provenance Visualization
 

Viewers also liked

AGLSP Conference Presentation
AGLSP Conference PresentationAGLSP Conference Presentation
AGLSP Conference Presentation
Tanyatoft
 
Cv L.S.Bhandary Eng
Cv L.S.Bhandary EngCv L.S.Bhandary Eng
Cv L.S.Bhandary Eng
lbhandary
 
Conectores_Slides
Conectores_SlidesConectores_Slides
Conectores_Slides
Rosannys
 
The Top 4 risks in P4P (Pay for Performance) 20120611
The Top 4 risks in P4P (Pay for Performance) 20120611The Top 4 risks in P4P (Pay for Performance) 20120611
The Top 4 risks in P4P (Pay for Performance) 20120611
PERFORMENSATION
 
Krishnan V Resume2
Krishnan V Resume2Krishnan V Resume2
Krishnan V Resume2
kris85venkat
 

Viewers also liked (20)

Optimize Oracle On VMware (Sep 2011)
Optimize Oracle On VMware (Sep 2011)Optimize Oracle On VMware (Sep 2011)
Optimize Oracle On VMware (Sep 2011)
 
AGLSP Conference Presentation
AGLSP Conference PresentationAGLSP Conference Presentation
AGLSP Conference Presentation
 
Artist training refugee class social media for musicians
Artist training refugee class social media for musiciansArtist training refugee class social media for musicians
Artist training refugee class social media for musicians
 
Smart Water & Sewer Systems: The Future of Utilities
Smart Water & Sewer Systems: The Future of UtilitiesSmart Water & Sewer Systems: The Future of Utilities
Smart Water & Sewer Systems: The Future of Utilities
 
Supplemental Info: Say on Pay and Dodd Frank 20100723
Supplemental Info: Say on Pay and Dodd Frank 20100723Supplemental Info: Say on Pay and Dodd Frank 20100723
Supplemental Info: Say on Pay and Dodd Frank 20100723
 
Performensation Blog Articles Jan - June 2011
Performensation Blog Articles Jan - June 2011Performensation Blog Articles Jan - June 2011
Performensation Blog Articles Jan - June 2011
 
Pró-Labore - Como aumentar o seu
Pró-Labore - Como aumentar o seuPró-Labore - Como aumentar o seu
Pró-Labore - Como aumentar o seu
 
Asset Management for Small Systems - AWWA Conference
Asset Management for Small Systems - AWWA ConferenceAsset Management for Small Systems - AWWA Conference
Asset Management for Small Systems - AWWA Conference
 
Rosa Et Al. 2010
Rosa Et Al. 2010Rosa Et Al. 2010
Rosa Et Al. 2010
 
Building a Community Around your Blog
Building a Community Around your BlogBuilding a Community Around your Blog
Building a Community Around your Blog
 
Digital Learners
Digital LearnersDigital Learners
Digital Learners
 
High Performance Plsql
High Performance PlsqlHigh Performance Plsql
High Performance Plsql
 
WordPress Development Confoo 2010
WordPress Development Confoo 2010WordPress Development Confoo 2010
WordPress Development Confoo 2010
 
Green Stormwater: LID with GIS
Green Stormwater: LID with GISGreen Stormwater: LID with GIS
Green Stormwater: LID with GIS
 
Cv L.S.Bhandary Eng
Cv L.S.Bhandary EngCv L.S.Bhandary Eng
Cv L.S.Bhandary Eng
 
Conectores_Slides
Conectores_SlidesConectores_Slides
Conectores_Slides
 
The Top 4 risks in P4P (Pay for Performance) 20120611
The Top 4 risks in P4P (Pay for Performance) 20120611The Top 4 risks in P4P (Pay for Performance) 20120611
The Top 4 risks in P4P (Pay for Performance) 20120611
 
Krishnan V Resume2
Krishnan V Resume2Krishnan V Resume2
Krishnan V Resume2
 
Imagine Cup 2009
Imagine Cup 2009Imagine Cup 2009
Imagine Cup 2009
 
Wastewater Treatment Systems-Public And Private
Wastewater Treatment Systems-Public And PrivateWastewater Treatment Systems-Public And Private
Wastewater Treatment Systems-Public And Private
 

Similar to Web analytics webinar

Getting the Most Out of Your E-Resources: Measuring Success
Getting the Most Out of Your E-Resources: Measuring SuccessGetting the Most Out of Your E-Resources: Measuring Success
Getting the Most Out of Your E-Resources: Measuring Success
kramsey
 
Being FAIR: FAIR data and model management SSBSS 2017 Summer School
Being FAIR:  FAIR data and model management SSBSS 2017 Summer SchoolBeing FAIR:  FAIR data and model management SSBSS 2017 Summer School
Being FAIR: FAIR data and model management SSBSS 2017 Summer School
Carole Goble
 
Advantages And Disadvantages Of Chronic Kidney Disease
Advantages And Disadvantages Of Chronic Kidney DiseaseAdvantages And Disadvantages Of Chronic Kidney Disease
Advantages And Disadvantages Of Chronic Kidney Disease
Karen Oliver
 
Data management plans (dmp) for nsf
Data management plans (dmp) for nsfData management plans (dmp) for nsf
Data management plans (dmp) for nsf
Brad Houston
 

Similar to Web analytics webinar (20)

What Is Log Analyis
What Is Log AnalyisWhat Is Log Analyis
What Is Log Analyis
 
UCIAD overview
UCIAD overviewUCIAD overview
UCIAD overview
 
Introduction to data science
Introduction to data scienceIntroduction to data science
Introduction to data science
 
Data collection, Data Integration, Data Understanding e Data Cleaning & Prepa...
Data collection, Data Integration, Data Understanding e Data Cleaning & Prepa...Data collection, Data Integration, Data Understanding e Data Cleaning & Prepa...
Data collection, Data Integration, Data Understanding e Data Cleaning & Prepa...
 
Acting as Advocate? Seven steps for libraries in the data decade
Acting as Advocate? Seven steps for libraries in the data decadeActing as Advocate? Seven steps for libraries in the data decade
Acting as Advocate? Seven steps for libraries in the data decade
 
Week-1-Introduction to Data Mining.pptx
Week-1-Introduction to Data Mining.pptxWeek-1-Introduction to Data Mining.pptx
Week-1-Introduction to Data Mining.pptx
 
Getting the Most Out of Your E-Resources: Measuring Success
Getting the Most Out of Your E-Resources: Measuring SuccessGetting the Most Out of Your E-Resources: Measuring Success
Getting the Most Out of Your E-Resources: Measuring Success
 
Being FAIR: FAIR data and model management SSBSS 2017 Summer School
Being FAIR:  FAIR data and model management SSBSS 2017 Summer SchoolBeing FAIR:  FAIR data and model management SSBSS 2017 Summer School
Being FAIR: FAIR data and model management SSBSS 2017 Summer School
 
In Search of a Missing Link in the Data Deluge vs. Data Scarcity Debate
In Search of a Missing Link in the Data Deluge vs. Data Scarcity DebateIn Search of a Missing Link in the Data Deluge vs. Data Scarcity Debate
In Search of a Missing Link in the Data Deluge vs. Data Scarcity Debate
 
Information entanglement
Information entanglementInformation entanglement
Information entanglement
 
Combining analytics and user research
Combining analytics and user researchCombining analytics and user research
Combining analytics and user research
 
A Distributed Architecture for Sharing Ecological Data Sets with Access and U...
A Distributed Architecture for Sharing Ecological Data Sets with Access and U...A Distributed Architecture for Sharing Ecological Data Sets with Access and U...
A Distributed Architecture for Sharing Ecological Data Sets with Access and U...
 
Business research (1)
Business research (1)Business research (1)
Business research (1)
 
Advantages And Disadvantages Of Chronic Kidney Disease
Advantages And Disadvantages Of Chronic Kidney DiseaseAdvantages And Disadvantages Of Chronic Kidney Disease
Advantages And Disadvantages Of Chronic Kidney Disease
 
business-research.ppt
business-research.pptbusiness-research.ppt
business-research.ppt
 
BLC & Digital Science: Jonathan Breeze, Symplectic
BLC & Digital Science: Jonathan Breeze, SymplecticBLC & Digital Science: Jonathan Breeze, Symplectic
BLC & Digital Science: Jonathan Breeze, Symplectic
 
Jonathan Breeze, Symplectic
Jonathan Breeze, SymplecticJonathan Breeze, Symplectic
Jonathan Breeze, Symplectic
 
Preservation Planning using Plato, by Hannes Kulovits and Andreas Rauber
Preservation Planning using Plato, by Hannes Kulovits and Andreas RauberPreservation Planning using Plato, by Hannes Kulovits and Andreas Rauber
Preservation Planning using Plato, by Hannes Kulovits and Andreas Rauber
 
UK Digital Curation Centre: enabling research data management at the coalface
UK Digital Curation Centre: enabling research data management at the coalfaceUK Digital Curation Centre: enabling research data management at the coalface
UK Digital Curation Centre: enabling research data management at the coalface
 
Data management plans (dmp) for nsf
Data management plans (dmp) for nsfData management plans (dmp) for nsf
Data management plans (dmp) for nsf
 

More from Jim Jansen

The Use of Query Reformulation to Predict Future User Actions
The Use of Query Reformulation to Predict Future User ActionsThe Use of Query Reformulation to Predict Future User Actions
The Use of Query Reformulation to Predict Future User Actions
Jim Jansen
 

More from Jim Jansen (15)

Networked Consumers: How networked and how important?
Networked Consumers:  How networked and how important?Networked Consumers:  How networked and how important?
Networked Consumers: How networked and how important?
 
Web analytics presentation
Web analytics presentationWeb analytics presentation
Web analytics presentation
 
Jjansen networked consumer_2011
Jjansen networked consumer_2011Jjansen networked consumer_2011
Jjansen networked consumer_2011
 
Twitter and EWOM Branding
Twitter and EWOM BrandingTwitter and EWOM Branding
Twitter and EWOM Branding
 
Lesson_04_ist402_google_adwords_02
Lesson_04_ist402_google_adwords_02Lesson_04_ist402_google_adwords_02
Lesson_04_ist402_google_adwords_02
 
Lesson 15 When Where To Show Your Ads
Lesson 15 When Where To Show Your AdsLesson 15 When Where To Show Your Ads
Lesson 15 When Where To Show Your Ads
 
Lesson 13 Writing Good Ads 02
Lesson 13 Writing Good Ads 02Lesson 13 Writing Good Ads 02
Lesson 13 Writing Good Ads 02
 
Lesson 11 Writing Good Ads
Lesson 11 Writing Good AdsLesson 11 Writing Good Ads
Lesson 11 Writing Good Ads
 
Lesson 07 Ist402 Keywords Take 02
Lesson 07 Ist402 Keywords Take 02Lesson 07 Ist402 Keywords Take 02
Lesson 07 Ist402 Keywords Take 02
 
Lesson 06 Ist402 Keywords 02
Lesson 06 Ist402 Keywords 02Lesson 06 Ist402 Keywords 02
Lesson 06 Ist402 Keywords 02
 
Lesson 05 Three Course Requirements
Lesson 05 Three Course RequirementsLesson 05 Three Course Requirements
Lesson 05 Three Course Requirements
 
lesson_03 Setting up Adwords Accounts, Adwords, and Selecting Businesses
lesson_03 Setting up Adwords Accounts, Adwords, and Selecting Businesseslesson_03 Setting up Adwords Accounts, Adwords, and Selecting Businesses
lesson_03 Setting up Adwords Accounts, Adwords, and Selecting Businesses
 
Ist402 Google Marketing Challenge V02
Ist402 Google Marketing Challenge V02Ist402 Google Marketing Challenge V02
Ist402 Google Marketing Challenge V02
 
The Use of Query Reformulation to Predict Future User Actions
The Use of Query Reformulation to Predict Future User ActionsThe Use of Query Reformulation to Predict Future User Actions
The Use of Query Reformulation to Predict Future User Actions
 
Profiling a Person With Search Log Data
Profiling a Person With Search Log DataProfiling a Person With Search Log Data
Profiling a Person With Search Log Data
 

Recently uploaded

What is social media.pdf Social media refers to digital platforms and applica...
What is social media.pdf Social media refers to digital platforms and applica...What is social media.pdf Social media refers to digital platforms and applica...
What is social media.pdf Social media refers to digital platforms and applica...
AnaBeatriz125525
 
NewBase 24 May 2024 Energy News issue - 1727 by Khaled Al Awadi_compresse...
NewBase   24 May  2024  Energy News issue - 1727 by Khaled Al Awadi_compresse...NewBase   24 May  2024  Energy News issue - 1727 by Khaled Al Awadi_compresse...
NewBase 24 May 2024 Energy News issue - 1727 by Khaled Al Awadi_compresse...
Khaled Al Awadi
 

Recently uploaded (20)

Unveiling Gemini: Traits and Personality of the Twins
Unveiling Gemini: Traits and Personality of the TwinsUnveiling Gemini: Traits and Personality of the Twins
Unveiling Gemini: Traits and Personality of the Twins
 
MichaelStarkes_UncutGemsProjectSummary.pdf
MichaelStarkes_UncutGemsProjectSummary.pdfMichaelStarkes_UncutGemsProjectSummary.pdf
MichaelStarkes_UncutGemsProjectSummary.pdf
 
What is social media.pdf Social media refers to digital platforms and applica...
What is social media.pdf Social media refers to digital platforms and applica...What is social media.pdf Social media refers to digital platforms and applica...
What is social media.pdf Social media refers to digital platforms and applica...
 
Falcon Invoice Discounting Setup for Small Businesses
Falcon Invoice Discounting Setup for Small BusinessesFalcon Invoice Discounting Setup for Small Businesses
Falcon Invoice Discounting Setup for Small Businesses
 
The Inspiring Personality To Watch In 2024.pdf
The Inspiring Personality To Watch In 2024.pdfThe Inspiring Personality To Watch In 2024.pdf
The Inspiring Personality To Watch In 2024.pdf
 
Pitch Deck Teardown: Terra One's $7.5m Seed deck
Pitch Deck Teardown: Terra One's $7.5m Seed deckPitch Deck Teardown: Terra One's $7.5m Seed deck
Pitch Deck Teardown: Terra One's $7.5m Seed deck
 
Potato Flakes Manufacturing Plant Project Report.pdf
Potato Flakes Manufacturing Plant Project Report.pdfPotato Flakes Manufacturing Plant Project Report.pdf
Potato Flakes Manufacturing Plant Project Report.pdf
 
FEXLE- Salesforce Field Service Lightning
FEXLE- Salesforce Field Service LightningFEXLE- Salesforce Field Service Lightning
FEXLE- Salesforce Field Service Lightning
 
NewBase 24 May 2024 Energy News issue - 1727 by Khaled Al Awadi_compresse...
NewBase   24 May  2024  Energy News issue - 1727 by Khaled Al Awadi_compresse...NewBase   24 May  2024  Energy News issue - 1727 by Khaled Al Awadi_compresse...
NewBase 24 May 2024 Energy News issue - 1727 by Khaled Al Awadi_compresse...
 
New Product Development.kjiy7ggbfdsddggo9lo
New Product Development.kjiy7ggbfdsddggo9loNew Product Development.kjiy7ggbfdsddggo9lo
New Product Development.kjiy7ggbfdsddggo9lo
 
A Brief Introduction About Jacob Badgett
A Brief Introduction About Jacob BadgettA Brief Introduction About Jacob Badgett
A Brief Introduction About Jacob Badgett
 
Innomantra Viewpoint - Building Moonshots : May-Jun 2024.pdf
Innomantra Viewpoint - Building Moonshots : May-Jun 2024.pdfInnomantra Viewpoint - Building Moonshots : May-Jun 2024.pdf
Innomantra Viewpoint - Building Moonshots : May-Jun 2024.pdf
 
The Truth About Dinesh Bafna's Situation.pdf
The Truth About Dinesh Bafna's Situation.pdfThe Truth About Dinesh Bafna's Situation.pdf
The Truth About Dinesh Bafna's Situation.pdf
 
Equinox Gold Corporate Deck May 24th 2024
Equinox Gold Corporate Deck May 24th 2024Equinox Gold Corporate Deck May 24th 2024
Equinox Gold Corporate Deck May 24th 2024
 
Event Report - IBM Think 2024 - It is all about AI and hybrid
Event Report - IBM Think 2024 - It is all about AI and hybridEvent Report - IBM Think 2024 - It is all about AI and hybrid
Event Report - IBM Think 2024 - It is all about AI and hybrid
 
Revolutionizing Industries: The Power of Carbon Components
Revolutionizing Industries: The Power of Carbon ComponentsRevolutionizing Industries: The Power of Carbon Components
Revolutionizing Industries: The Power of Carbon Components
 
Unlock Your TikTok Potential: Free TikTok Likes with InstBlast
Unlock Your TikTok Potential: Free TikTok Likes with InstBlastUnlock Your TikTok Potential: Free TikTok Likes with InstBlast
Unlock Your TikTok Potential: Free TikTok Likes with InstBlast
 
Sedex Members Ethical Trade Audit (SMETA) Measurement Criteria
Sedex Members Ethical Trade Audit (SMETA) Measurement CriteriaSedex Members Ethical Trade Audit (SMETA) Measurement Criteria
Sedex Members Ethical Trade Audit (SMETA) Measurement Criteria
 
Creative Ideas for Interactive Team Presentations
Creative Ideas for Interactive Team PresentationsCreative Ideas for Interactive Team Presentations
Creative Ideas for Interactive Team Presentations
 
LinkedIn Masterclass Techweek 2024 v4.1.pptx
LinkedIn Masterclass Techweek 2024 v4.1.pptxLinkedIn Masterclass Techweek 2024 v4.1.pptx
LinkedIn Masterclass Techweek 2024 v4.1.pptx
 

Web analytics webinar

  • 1. Web Analytics Jim Jansen Associate Professor, The Pennsylvania State University
  • 2.
  • 3.
  • 4.
  • 5. How much is a Zettabyte?
  • 6.
  • 7.
  • 8.
  • 9.
  • 10.
  • 11.
  • 12. W3C Extended Log Format -Variety of fields for examining visitors to Web sites. Other common format is NCSA Separate Log that is composed of three logs Common log – actions on the server, Referral log – where they came from, and Agent log – stuff about the client computer Rather than service-side logging, other methods such as page tagging, image cookies, Flash cookies, etc. but the data is still stored in a log. W3C Extended Log Format
  • 13.
  • 14. Variety of tools help make sense of this log data
  • 15.
  • 16.
  • 17.
  • 18.
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40.
  • 41.
  • 42.
  • 43. Thanks! (welcome questions / discussion!) Web Analytics Jim Jansen Associate Professor, The Pennsylvania State University
  • 44.
  • 45.
  • 46. Again, thanks! Web Analytics Jim Jansen Associate Professor, The Pennsylvania State University